The remarkable aspect of mainstream economists is their persistence treating values in (digital) ledgers as actual instances of banknotes (physical paper) as their mental model, then selectively dropping the model.
These are really good questions that actually would be best addressed by agent-based modeling, where individual agents (banks, employees, investors, bond traders, federal govt, state govt, firms etc) each have behaviors defined by rules and you run the system adjusting the rules to see how the scenario plays out. #julialang is a great tool for this using the Agents.jl library in part because it's fast
(compared to python when it is forced to apply arbitrary functions with loops inside, element-wise to an array - that is, can't benefit from vectorised numpy functions)
this #maths experiment took about an hour in python and about 1 second in julia lang
sure my python isn't professional, but today was my first time with julia lang so that will be far from optimal either
Seeing the schedule in #julialang conference and the last survey, looks like the community is embracing more the idea of using Rust as a support language.
Getting there. These images show tests of a triangulation algorithm I've developed that uses Delaunay triangulation. It features mostly equilateral triangles except for at the boundaries.
We can't replace them, but we welcome anyone looking for a friendly, inclusive community to join us at the Data Science Learning Community (@DSLC) https://DSLC.io
My university dropped the campus wide MATLAB license around August last year. It is amusing to see the effect on my GitHub contribution chart. But then I picked up #julialang and now there is more than a recovery :)
I kept my twtr account for a while because brands I occasionally reach out to were still exclusively there. It’s now no longer the case so I put the account down for real :)
@ayo Thanks for building this @anze3db, I've already found some great accounts to follow! A couple of things that would make it even better, the Nix language uses a regex that matches people mentioning Unix or *nix, so it has a lot of false positives. Secondly, I'd love to see #JuliaLang added to the list of languages, and to add JuliaLang.social to the index :)
Makie is a data visualization ecosystem for the Julia programming language, with high performance and extensibility. It supports various data visualization applications like 2D, 3D, and geospatial plots.
The R4DS Online Learning Community has thousands of members, hundreds of which are active on our Slack every week. You might be wondering: Why not charge those learners? Why is the Community funded through donations?
One of my favorite hard #scifi topics that I love to work within (even if I am terrifically starved for time to do much about this at the moment) are wormhole networks.
They have interesting geometry constraints once you add real star maps, #optimization challenges when it becomes time to figure out how many connections you need to connect what places while observing construction and shape constraints…
it is the kind of #graph computer science that tickles my brain extra being scifi nerdy
In particular, simulating n-agent circumstances with improving technologies (wormhole projection speeds and ranges in a single connection creation) would be interesting, both to study optimal networks that satisfy the most constraints from all the parties, and just letting some organically emerge with quirks and bad choices that play off of each other.
Maybe something to use #julialang for, though I have to say, when it comes to really optimizing the configuration, I lack know-how
#Julia#Julialang@julialang
I have a question:
I am using HDF5.jl to open an hdf5 file; the file is organised in >100 datasets, written by the creator in a rather unpredictable way.
How do I automatically open all
datasets in the sequence, without knowing their name beforehand? (with the final goal of recreating a single long array, by appending the content of each dataset - this I know how to do).
thanks in advance
My weekend side-project: at long last I set some time aside to learn the basics of #julialang. The learning process was a tonne of fun. The thing where it somehow sprawled into a three-part series of blog posts... not so much fun. Anyway here they are... first one is me wrestling with some basic features in language
Second one is a bit more practical, and looks at how to do basic data wrangling using the DataFrames package. Not surprisingly as an #rstats person learning #julialang, a big part of this was me trying to find a pipe-centric workflow that I like. In the end I decided I kind of like the combination of the Julia base pipe and anonymous functions as a workflow
By the time I hit the third post I was kind of exhausted, so it's a bit, um, telegraphic in places. But nevertheless it was sort of nice to get a sense of (one tool for) data visualisation in #julialang
I wonder how #Mojo can get away with lies regarding its latest benchmark.
Fake news in tech are actually a threat?
Tech entertainers like #ThePrimagen only make things worse by promoting these fake news without validation.
Then he is quoted in a later Mojo blog post spreading bullshit about #RustLang and ignoring #JuliaLang. But debunking posts like this are ignored: